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Chronic Kidney Disease (CKD) is a heterogeneous disorder that gradually affects the structure and function of the kidneys, is difficult to recover, and causes the body to be unable to maintain metabolism and fail to maintain fluid and electrolyte balance, leading to increased urea levels. Chronic kidney disease data was obtained from Kaggle, in this study a comparison was made between two classification algorithms, namely Naïve Bayes Classifier (NBC) and Random Forest because it is not yet known what algorithm is best in classifying chronic kidney disease (CKD). Both algorithms are evaluated based on performance metrics such as accuracy, precision, recall, and confusion matrix. The results of the evaluation showed that in a dataset of 400 samples, the performance  of the Naïve Bayes Classifier (NBC) algorithm obtained an accuracy of 94%, while Random Forest had an accuracy of 93%. Then in the small dataset (158 data), Random Forest got a better accuracy score with 87% compared to the Naïve Bayes Classifier (NBC) of 78%. Based on the results of the evaluation, Random Forest has a more stable performance on small datasets, while Naïve Bayes Classifier (NBC) provides higher performance on larger datasets in the context of chronic kidney disease classification.

This study aims to analyze the effect of using cassava (Manihot esculenta crantz) as a carbohydrate-based feed ingredient on body weight gain in beef cattle. The review was conducted using a descriptive literature study approach based on sixteen scientific articles discussing the nutritional composition, processing methods, and performance responses of beef cattle fed cassava-based diets. The analysis shows that cassava contains 17.45–88.6% dry matter, 2.4–21.45% crude protein, and 11.35–92.2% nitrogen-free extract, with variations influenced by plant part, processing method, and hydrocyanic acid (HCN) content. Processing techniques such as fermentation and ensiling can reduce HCN levels by more than 70% while increasing crude protein content up to 25%, thereby improving digestibility and feed efficiency. The inclusion of cassava in the form of flour, dried chips, pulp, or fermented peel consistently enhances dry matter intake and average daily gain (ADG) of beef cattle at inclusion levels of 20–50% in the diet. Overall, cassava has strong potential as a locally available, economical, and sustainable feed ingredient to improve beef cattle productivity.

Amidst the current changing global conditions, it is important for a country to achieve the Sustainable Development Goals (SDGs) to face challenges in sustainable development, social inequality, and strengthen economic and environmental resilience. This study aims to analyze the influence of environmental performance and political stability on the SDG scores of ASEAN countries for the 2020-2024 period, moderated by economic growth. Researchers used a quantitative method, processed using multiple linear regression with SPSS. The regression process was conducted twice, before and after using moderating variables. The findings suggest that economic growth can alter the influence of environmental performance and political stability on SDG scores. Political stability has a positive impact on the SDGs after economic growth has moderated. While environmental performance has a negative impact after being moderated by economic growth. Economic growth promotes political stability and sustainable growth. Conversely, with high growth, improvements in environmental performance are indicated to shift priorities from sustainability to exploitation.

The principle of comparative advantage explains that every country or society, like individuals, can gain benefits from their trade activities by exporting goods or services in which they have a major comparative advantage and importing goods or services in which they do not. Based on the law of comparative advantage, even though a country may be less efficient (having an absolute disadvantage) compared to other countries in the production process, the structure of industrial performance can be seen through the analysis of industrial sector behavior analyzed through various strategies such as Price, Product, and promotion. The theory of comparative advantage related to the exchange of goods is relevant as long as the traded goods are still useful. In other words, Performance is defined as the result of activities influenced by the structure and behavior within the industrial sector, where these results are often measured by the size of a company's market share or profitability in an industry. In more detail, performance can also be reflected in the form of efficiency, development (including market expansion), job creation, employee welfare, and a sense of group pride.

The problem faced is the decline in the performance of a 650 liter/minute centrifugal pump due to wear on components, especially the impeller, rolling bearings, and mechanical seals in supplying process water and clean water in industrial production systems. The planning objective is to obtain a maintenance schedule for a 650 liter/minute centrifugal pump for the operational period of 2026 and the ratio between maintenance costs and profits generated by the machine. The maintenance planning method includes collecting maintenance data from previous maintenance periods, reviewing centrifugal pump specifications, applying the inspection, replace, repair, and overhaul (IRRO) approach, estimating the age and price of components that are expected to be damaged, estimating the cost and duration of dismantling and installing components that have been repaired in accordance with the provisions of the requirements for usable components or replacement parts, scheduling maintenance and repairs, estimating maintenance and repair costs for the 2026 period, and determining the ratio of maintenance costs to profits. The planning results are in the form of a maintenance schedule for the 2026 period worth IDR 4,290,000,-, a maintenance cost to profit ratio of 7.44% and the implications indicate that the machine is still suitable for use and prospective for operations in the next few years.  

This study aims to analyze the application of the franchising system on business performance in the beverage franchise business through a case study of Mitra Minuman Siap Saji. The approach used is qualitative with a case study design, with data collection techniques in the form of in-depth interviews, operational observations, and supporting documentation. The results show that the implementation of standardized Standard Operating Procedures (SOPs), franchisor support in the form of training, raw material supplies, and periodic monitoring contribute significantly to improving business stability, product quality consistency, and customer satisfaction. However, there are limitations in flexibility and several communication obstacles that have the potential to affect the effectiveness of the partnership. The relatively strict contract structure also impacts partners' perceptions of local innovation space, although it is generally still viewed as providing business security and business model clarity. These findings emphasize that a balance between franchisor control and partner autonomy, accompanied by open communication and fair contract design, is a key factor in creating sustainable business performance in a franchising system.

Increasing traffic activity at unsignalized intersections often results in congestion and delays. The intersection of Jl. Imam Bonjol and Jl. Daya Nasional experiences these conditions, making performance evaluation necessary. This research tends to scrutinize the intersection performance under existing conditions and five-year projected conditions and to identify appropriate improvement measures. The analysis was carried out using the 2023 Indonesian Road Capacity Guidelines (PKJI 2023) and PTV VISSIM microsimulation software. Data on intersection geometry, vehicle speed, and traffic volume were collected through CCTV observations on weekdays and weekends. The results show that under existing conditions, the degree of saturation reached 1.170 with an average delay of 33.75 seconds (LOS D). In the five-year projection, the degree of saturation increased to 1.239 with an average delay of 53.69 seconds (LOS E). These findings indicate a decline in intersection performance, highlighting the need for alternative traffic management measures to improve operational performance and service levels.    

This study seeks to examine how the element of state financial loss is applied in corruption cases that originate from private-law relationships, specifically in the context of Micro People’s Business Credit (KUR) financing using the Yarnen Porang scheme, as reflected in Decision Number 41/Pid.Sus.TPK/PN.MTR. The central issue addressed is the manner in which the panel of judges construed and affirmed the existence of a state financial loss that, in substance, arose from a civil act, namely the performance of a financing cooperation agreement between a banking institution and an offtaker. By employing a normative juridical approach and conducting an in-depth analysis of the judicial decision, this research concludes that the alleged state financial loss in the a quo case remains merely prospective in nature and does not satisfy the requirement of an actual and definite loss as mandated by positive law. Furthermore, evidence demonstrating that the financing funds were enjoyed by a third party rather than by the accused indicates a misapplication in attributing criminal liability. Consequently, the criminal prosecution of conduct that is essentially civil in character reflects an expansive interpretation of the state loss element, which is inconsistent with the principle of legality and the doctrine of prudence in the enforcement of corruption laws.

Traffic congestion is a persistent challenge in urban areas in Indonesia, where increasing vehicle density creates the need for intelligent traffic monitoring systems. This study aims to develop a real-time vehicle parking system using the YOLOv8 object detection model to provide efficient traffic analysis from live CCTV broadcasts and recorded videos. This study uses a quantitative experimental approach with the implementation of the YOLOv8m model using the Ultralytics library in Python, tested on data collected from CCTV cameras A TCS Dishub Medan and additional footage from mobile devices. Vehicles are detected and counted in two directions up (Up) and down (Down) using virtual detection lines on the video frame. The system performance is evaluated by automatic detection counting with manually recorded ground truth data. The results show that on live CCTV broadcasts, the YOLOv8m model achieves an average precision of 98.96%, a recall of 96.59%, and an F1 score of 97.74% for upstream traffic, while for downstream traffic it achieves 100% precision, 95.64% recall, and an F1 score of 97.730/0. On the other hand, on high-quality recorded videos, all performance metrics achieve 100%, indicating perfect detection accuracy. These findings confirm the effectiveness of YOLOv8 in real-time traffic monitoring, but also indicate that video quality and stream stability affect detection performance. In conclusion, the developed system shows strong potential to support smart city traffic management solutions. Future research should focus on performance optimization under low-resolution live streaming conditions to improve accuracy in practical applications.  

In social life, buying and selling are fundamental mechanisms for transferring rights, beginning with an agreement. According to Articles 1313 and 1338 of the Indonesian Civil Code, agreements are legally binding acts with the force of law for the parties involved. One high-value transaction is the sale and purchase of apartment units, which involves developers as sellers. In practice, developers often fail to deliver units within the agreed timeframe. This study examines (1) the developer’s responsibility toward buyers when units are not delivered and (2) the legal protection available for buyers under such circumstances. The research applies Hans Kelsen’s Theory of Responsibility and Satjipto Rahardjo’s Theory of Legal Protection, using a normative juridical method based on library research. Primary, secondary, and tertiary legal materials were analyzed through statutory, conceptual, analytical, and case approaches, employing grammatical and systematic interpretation, legal analogy, and legal refinement. Findings reveal that developers are primarily responsible for delivering fully paid units. Failure to fulfill this obligation, due to breach of contract or negligence, triggers legal liability in the form of performance or compensation. Legal protection for buyers ensures their rights are safeguarded, and even in cases of developer negligence or bankruptcy, consumers are legally entitled to receive the apartment units they have purchased.

AImbalanced data remains a significant issue in heart disease classification using machine learning, as it tends to cause models to overestimate the majority class while ignoring minority classes with high clinical value. This can lead to a decrease in accuracy and the model's ability to accurately detect disease cases. Therefore, this study aims to assess the effectiveness of oversampling techniques, namely Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE), in improving the performance of the K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF) algorithms. The dataset used comes from Kaggle and consists of 918 data sets with 12 attributes representing patient information related to heart disease prediction. The research stages include data preprocessing, baseline model testing, and re-evaluation using the two oversampling methods. Experimental results show that oversampling can improve the performance of all algorithms. KNN achieved the best results with SMOTE, with an accuracy of 72.98% and an F1-score of 75.39%. In the Naive Bayes algorithm, both oversampling techniques produced relatively stable performance, with the highest F1-score of 73.56% using SMOTE. Meanwhile, Random Forest showed the most optimal performance when combined with Random Oversampling, with an accuracy of 79.19% and an F1-score of 81.51%. These findings confirm that the success of data balancing techniques is strongly influenced by the characteristics of the classification algorithm used, and provide a practical contribution in determining strategies for handling imbalanced data in health research.

This study investigates the impact of board characteristics on the financial performance of non-financial companies listed in the JII70 index in Indonesia. Motivated by the ongoing debate on the effectiveness of corporate governance mechanisms in enhancing firm outcomes, particularly within Sharia-compliant markets, this study focuses on three key board attributes: board size, board independence, and female representation on the board. Using a quantitative causal approach and panel data from 25 companies over the period 2020–2023, the study employs a fixed effect model to evaluate the relationship between board structure and financial performance measured by Return on Assets (ROA). The results show that board size has a positive and significant effect on firm performance, indicating that larger boards may enhance oversight capacity and provide broader resources beneficial to strategic decision-making. Conversely, board independence and board female representation do not exhibit significant effects on financial performance, suggesting that their roles may be more symbolic or constrained by institutional and contextual factors in the sampled companies. These findings highlight the importance of understanding corporate governance not merely in structural terms, but in relation to functional effectiveness and contextual maturity. The study offers implications for regulators, companies, and governance reform initiatives, particularly regarding strengthening substantive roles of independent and female commissioners in improving firm performance within Sharia-compliant markets.

This study explores how the organizational culture of Sanggar Kartika Budaya strengthens local artistic values through identity building, leadership, training strategies, and adaptive creativity. Rooted in a commitment to traditional arts, the sanggar positions local cultural expression not only as heritage but also as a living space for innovation. The research aims to uncover how these cultural elements shape member behavior, sustain artistic traditions, and support the regeneration of young artists. Using a qualitative approach with document analysis, this study examines official profiles, program descriptions, and relevant scholarly sources. The findings reveal that the sanggar’s cultural identity centered on the motto “Pegang Teguh Seni Tradisi Siap Berkreasi”serves as the backbone of its learning system and creative ecosystem. Leadership plays a central role in directing artistic vision while safeguarding cultural authenticity. Structured training, literacy activities, and collaborative performances effectively embed traditional values in new members. The sanggar also demonstrates an ability to evolve with modern trends through creative choreography, multimedia integration, and active participation in contemporary festivals, all while maintaining strong roots in local heritage. These findings highlight how a well-structured organizational culture can act as a powerful engine for cultural preservation and artistic resilience. The implications suggest that cultural institutions can remain relevant in a fast-changing era by blending heritage with innovation, ensuring that tradition continues to live meaningfully in the hands of future generations.

Sweet corn is a horticultural commodity that is widely consumed in Indonesia. This study evaluated the effectiveness of NPK 18-18-18 fertilizer on the vegetative growth, yield components, and relative agronomic effectiveness (RAE) of sweet corn (Zea mays saccharata). Field experiments were conducted using fertilizer dosages of 0,50, 0,75, 1,00, and 1,50 NPK, with a control treatment for comparison. The application of NPK 18-18-18 significantly increased plant height, stem diameter, leaf number, ear length, ear diameter, biomass weight, ear weight with husk, ear weight without husk, plot yield, and overall productivity compared with the control. Mean values across treatments ranged from 68,94–205,72 cm for plant height, 7,41–20,47 mm for stem diameter, 6,01–13,00 leaves per plant, 15,41–20,89 cm for ear length, and 36,05–49,65 mm for ear diameter. Biomass weight ranged from 0,12–0,34 kg, ear weight with husk from 0,13–0,34 kg, and ear weight without husk from 0,12–0,28 kg. Plot yield varied between 7,91–25,46 kg, corresponding to productivity levels of 5,02–16,16 t/ha. RAE analysis indicated that fertilizer application was effective at dosages of 0,75, 1,00, and 1,50 NPK, with the highest effectiveness observed at 1,50 NPK (118%). Notably, the 0,75 NPK dosage achieved an RAE value of 101%, demonstrating that lower fertilizer input can enhance yield while reducing production costs and mitigating fertilizer scarcity. These findings suggest that NPK 18-18-18 fertilizer, when applied at an optimal dosage, can be effectively utilized in sweet corn cultivation to improve growth and productivity while ensuring efficient nutrient management.

As a high-value crop, potatoes necessitate balanced nutrient management for optimal growth and yield. This research aimed to assess how varying applications of NPK 20-20-10 fertilizer influenced potato growth, yield, tuber quality, agronomic efficiency, and economic viability within tropical climates. The experimental setup involved a randomized complete block design, incorporating four replications across seven distinct treatments: a control, a standard inorganic fertilization regimen, and NPK 20-20-10 applied at 0.50, 0.75, 1.00, 1.25, and 1.50 times the suggested dosage. The findings indicated that applying NPK 20-20-10 significantly enhanced several parameters, including plant height, branch count, tuber count, tuber weight, and overall yield components, when contrasted with the control group. Notably, the 1.25 times recommended dose demonstrated superior performance, leading to a 34.9% increase in tuber number and a 68.6% rise in tuber weight compared to the control. Agronomic effectiveness scores surpassed 100 for dosages ranging from 0.75 to 1.50, with the 1.25 dose registering the peak value. Economic evaluations confirmed the profitability of all NPK treatments, and the 1.25 dose yielded the most favorable R/C ratio and a net profit of IDR 29,053,400. Consequently, the recommended application for potato cultivation is 675 kg/ha of NPK 20-20-10, distributed in three equal parts at planting, four weeks post-planting, and six weeks post-planting. Thus, these results underscore that NPK 20-20-10, when applied at 1.25 times the recommended rate, presents an agronomically effective and economically sound strategy for sustainable potato farming in tropical settings.

The issue of street vendors (PKL) in Sidoarjo Regency, particularly in the Gading Fajar area, has become a significant concern as it relates to public order, the use of public space, and the economic dynamics of small communities. To address these challenges, the Sidoarjo Regency Government enacted Regional Regulation Number 3 of 2016 as the legal basis for structuring and empowering PKL. This study examines how the regulation is implemented in practice and evaluates its effectiveness using a qualitative descriptive method through interviews, observations, and documentation, analyzed with Leo Agustino’s policy evaluation model, which assesses five key aspects: administrative resources, institutional coordination, infrastructure and technology, financial support, and regulatory adequacy. The findings show that the implementation of the regulation has not yet reached optimal performance, as limited socialization, insufficient personnel, weak coordination among agencies, inadequate supporting facilities, and low compliance with zoning rules hinder the achievement of policy objectives. These issues are reflected in the continued presence of PKL operating in non-designated areas despite clear regulatory provisions. Therefore, strengthening institutional capacity, improving facilities, enhancing enforcement consistency, and developing more operational technical guidelines are essential to ensure more effective and sustainable management and empowerment of street vendors in Sidoarjo Regency.

Diseases in primary health services exhibit complex spatial-temporal dynamics due to urbanization and population mobility. Conventional surveillance approaches are difficult to capture these patterns adaptively. Machine learning (ML) based on spatio-temporal modeling offers a solution with the ability to detect disease clusters automatically and with high precision. Research Objectives: This research aims to develop a machine learning model to detect disease hotspots from primary service data in Indonesia, with a focus on improving prediction accuracy, interpretability, and relevance of health policies. Methodology: The primary service dataset for 2024 (5,343 entries) was analyzed using three ML models Gradient Boosting Machine (GBM), Temporal Random Forest (TRF), and Multi-EigenSpot with spatial (village) and temporal (week, month) features. Performance evaluation includes predictive (AUC, F1-score) and spatial (Moran's I, Spatio-Temporal Correlation Index) metrics. Results: The results showed that Multi-EigenSpot achieved the best performance (AUC=0.91; F1=0.86), with the detection of dominant hotspots in Sungai Asam and Beringin Villages. Moran's I value of 0.63 indicates a strong spatial autocorrelation, while STCI=0.57 indicates moderate temporal stability. Conclusions: ML-based spatio-temporal models are effective in identifying hidden disease patterns and have the potential to be integrated into national digital surveillance systems. This approach supports precision public health by providing a scientific basis for real-time location- and time-based intervention policies.

This study aims to analyze the influence of Innovative Work Behaviour, Upskilling, and Work Ethic on the Health Workers Performance with Intention to Stay as an intervening variable at RSUD Sumberrejo. The background of this research stems from challenges in improving service quality, high workload, and the need to strengthen competency and retention among health workers. The study employs a quantitative approach using a survey method through the distribution of questionnaires, with data analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) through SmartPLS version 4.0. The population consists of 216 employees, and the sampling technique used is non-probability sampling, resulting in 140 respondents. The findings reveal that Innovative Work Behaviour, Work Ethic, and Intention to Stay have a significant positive effect on the performance of health workers. In addition, Innovative Work Behaviour and Work Ethic significantly influence Intention to Stay. However, Upskilling shows a positive but non-significant effect on both Intention to Stay and Performance, indicating that skill enhancement requires managerial support and motivation to contribute effectively to employee performance. Intention to Stay serves as a mediating variable in several relationships among the constructs. Strengthening innovative behaviour, work ethic, and competency development, accompanied by appropriate retention strategies, is essential for improving the performance of health workers in regional hospitals. Future studies are recommended to develop the research model by incorporating additional variables that may have stronger effects on Intention to Stay and Performance.

Physical fitness is a fundamental determinant of athletic performance and is strongly influenced by dietary intake and lifestyle behaviors. Adequate protein consumption is essential for muscle development and energy metabolism, whereas smoking has detrimental effects on lung function and aerobic capacity. This study aimed to explore the relationship between protein intake and smoking habits with physical fitness, measured by maximal oxygen uptake, among athletes of Persela Football Academy under-eighteen. A quantitative approach with a cross-sectional design was applied, involving adolescent male athletes. Protein intake was assessed using a semi-quantitative food frequency questionnaire, smoking habits were obtained through structured interviews, and maximal oxygen uptake was measured using the multistage fitness test. Findings revealed that the average daily protein intake of athletes was relatively high, while the mean maximal oxygen uptake score fell within the good category. Correlation analysis demonstrated a significant association between protein intake and aerobic fitness, as well as between smoking habits and aerobic fitness. The results indicate that lower protein intake and higher smoking frequency are linked to reduced physical fitness capacity. This study highlights the importance of nutritional interventions and healthy lifestyle promotion as integral components in the development of youth athletes to optimize performance and prevent decline in fitness.

Gestational Diabetes Mellitus (GDM) is a pregnancy-related metabolic disorder that poses health risks to both mother and fetus if not detected early, requiring accurate prediction methods for early screening and clinical decision-making. This study applies the Random Forest algorithm to detect GDM risk using clinical data from the Pima Indian Dataset. Data preprocessing included handling missing values, standardization, feature engineering, and a 70:30 train–test split. Two models were developed: a baseline and an optimized model using GridSearchCV hyperparameter tuning, validated with 5-fold cross-validation. Performance was assessed using a classification report, confusion matrix, and ROC–AUC. Results show that the optimized model outperforms the baseline, achieving 88% accuracy, an AUC of  93%, and average recall of 81%–85%. Compared to previous studies, this approach demonstrates improved predictive performance. The findings indicate that combining Random Forest with comprehensive preprocessing, feature engineering, and model optimization is effective and feasible for developing a medical decision support system for early GDM risk screening.